{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "import json\n",
    "\n",
    "title_list = []\n",
    "price_list = []\n",
    "with open(\"../myscrapy/book.json\",\"r\",encoding=\"utf-8\") as f:\n",
    "    con = f.read()\n",
    "    js_data = json.loads(con)\n",
    "    for i in js_data:\n",
    "        title_list.append(i[\"title\"])\n",
    "        price_list.append(i[\"price\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "\n",
    "# 示例数据\n",
    "# title_list = ['Item 1', 'Item 2', 'Item 3', 'Item 4', 'Item 5', 'Item 6']  # 增加一个标题\n",
    "# price_list = ['\\$10.50', '\\$20.00', '\\$15.75', '\\$30.25', '\\$25.00', '']  # 添加一个空字符串示例\n",
    "\n",
    "# 创建 DataFrame\n",
    "df = pd.DataFrame({\"title\": title_list, \"price\": price_list})\n",
    "\n",
    "# 转换价格为浮点数，处理空字符串\n",
    "def convert_price(price_str):\n",
    "    if price_str:\n",
    "        try:\n",
    "            return float(price_str[1:])  # 去掉货币符号并转换为浮点数\n",
    "        except ValueError:\n",
    "            return 0.0  # 如果转换失败，返回0.0\n",
    "    return 0.0  # 如果是空字符串，返回0.0\n",
    "\n",
    "df[\"price\"] = df[\"price\"].apply(convert_price)\n",
    "\n",
    "# 绘制直方图\n",
    "sns.histplot(x=\"price\", bins=10, data=df)\n",
    "plt.xlabel('Price ($)')\n",
    "plt.ylabel('Frequency')\n",
    "plt.title('Price Distribution')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "base",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.20"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
